抄録

This paper proposes a continuous-time model estimation method by using the Unscented Kalman Filter (UKF) from sampled I/O data, in which plant parameters as well as the initial state are estimated. The initial state is estimated based on a backward system of the plant, and the parameters are estimated by using an iterated UKF, which repeats the estimation of the forward system and the backward system alternately. In order to demonstrate the effectiveness of the proposed method, a rotary pendulum is considered to estimate the parameters of a continuous-time nonlinear system.